Position OverviewWe are seeking a VP of Research & Development to lead the transformation of our engineering organization into an AI-first, cloud-native delivery engine. This executive will drive the transition from a traditional software development lifecycle (SDLC) to a modern AI Development Lifecycle (AI-DLC) embedding ML pipelines, intelligent automation, and data-driven decision-making into every stage of product development on Azure cloud infrastructure.This role combines strategic leadership with hands-on technical direction. You will own the R&D vision, architect scalable AI/ML systems, align technology investments with business outcomes, and build a high-performance team capable of executing at speed while maintaining trust, security, and reliability. The ideal candidate brings deep expertise in MLOps, cloud-native architecture, and GenAI integration not just as concepts, but as production realities.Roles & ResponsibilitiesAI-DLC Transformation & InnovationLead the transition from SDLC to AI-DLC, integrating data pipelines, model development, evaluation, deployment, monitoring, and iteration into core engineering workflowsDesign and implement end-to-end ML pipeline orchestration using Azure ML, MLflow, or Kubeflow from feature engineering through model servingEstablish MLOps practices: model versioning, automated retraining triggers, A/B testing of models in production, drift detection, and performance monitoringDefine AI-DLC quality gating: automated checkpoints at each lifecycle stage (data validation, model evaluation, deployment readiness)Implement continuous evaluation loops production model performance monitoring against ground truth with automated rollback triggersBuild AI testing frameworks: unit testing for models, integration testing for pipelines, adversarial testing for robustnessArchitect RAG pipeline designs including chunking strategies, embedding model selection, retrieval optimization, and rerankingEstablish agentic AI design patterns multi-agent orchestration, tool use, and autonomous workflow executionDefine prompt management systems: version-controlled prompt libraries, A/B testing of prompts in productionLead fine-tuning vs. RAG vs. in-context learning decision frameworks knowing when each approach fitsImplement LLM evaluation frameworks: hallucination detection, factual grounding, response quality scoringDesign guardrails and content filtering: input/output validation, toxicity detection, PII redaction in LLM responsesDrive synthetic data generation strategies for training and testing where production data is restrictedArchitect vector database solutions and semantic search implementation (Pinecone, Azure AI Search, Weaviate)Apply model compression and optimization for edge deployment quantization, distillation, pruningOptimize token cost and inference scaling strategies across AI workloadsTechnology Strategy & Cloud ArchitectureIn collaboration with Product, define and execute the long-term technology roadmap aligned to AI-native deliveryArchitect Azure cloud-native infrastructure: AKS (Kubernetes), Azure Functions, Cosmos DB, Azure AI Services, Azure OpenAI ServiceDrive Infrastructure as Code (Terraform, Bicep) and GitOps deployment modelsDesign API-first architecture and microservices patterns for scalable, real-time, AI-enabled platformsImplement data pipeline architecture ETL/ELT modernization from batch-heavy to event-driven, real-time enrichment using Azure Event Hubs / KafkaApply data mesh principles: domain-owned data products, federated governance, self-serve data infrastructureEstablish feature stores for ML centralized, versioned, reusable feature engineering (Feast, Azure ML feature store)Build data quality frameworks: automated schema validation, anomaly detection, lineage trackingDesign multi-region deployment strategies for global mobility use cases (latency, data residency, failover)Lead edge computing and IoT integration strategies relevant to the company's device and hardware footprintImplement FinOps discipline: cost modeling for AI workloads, GPU compute optimization, spot instance strategies, reserved capacity planningDrive multi-modal AI capabilities vision, document understanding, speech-to-text integrationProduct & Business AlignmentPartner with Product, Sales, and Customer Success to deliver impactful, customer-centric AI-powered solutionsTranslate global mobility use cases into scalable technical solutions with measurable business impactAlign R&D priorities with company growth targets and market opportunitiesLead build vs. buy vs. partner evaluation frameworks specifically for AI capabilitiesGovernance, Security & TrustImplement AI governance frameworks covering privacy, compliance, and model risk managementApply Zero Trust security architecture to AI workloads identity-based access, secrets management (Azure Key Vault), network segmentationEstablish model risk management aligned to regulatory expectations (EU AI Act awareness, SOC 2 implications)Build AI audit trails: decision logging, reproducibility, explainability-on-demand for regulated use casesDeploy responsible AI tooling: explainability (SHAP, LIME), bias detection, model cardsImplement ethical AI review checkpoints before production deploymentConduct third-party AI vendor risk assessments evaluating external models, APIs, and data providers for security, reliability, and IP exposureDefine IP strategy for AI outputs: ownership of model weights, generated content, and training data derivativesManage governance of training data provenance tracking, PII handling, consent management, right-to-be-forgotten complianceLeadership & Team DevelopmentBuild and lead a multidisciplinary team spanning software engineering, data engineering, ML engineering, embedded systems, and AI/ML researchFoster a culture of innovation, accountability, ownership, and continuous improvementRecruit and develop top-tier technical talent with AI/ML depthDrive cross-functional sprint models data engineers, ML engineers, product, and domain experts operating as one unit, not silosLaunch AI-assisted development tooling adoption across R&D (Copilot, Claude Code, automated code review)Baseline engineering team AI fluency and build structured upskilling programsPromote inner-source practices: shared libraries, reusable components, internal API marketplacesOperational ExcellenceImprove delivery speed, reliability, and product quality through CI/CD automation, platform engineering, and observabilityImplement SRE practices: SLOs, error budgets, incident management frameworksEstablish DORA metrics as the engineering performance framework (deployment frequency, lead time, change failure rate, MTTR)Deploy observability stack: distributed tracing, structured logging, metrics dashboardsImplement feature flagging, canary deployments, and progressive rollout strategiesPrioritize developer experience (DX) as a first-class engineering investmentQuantify and manage technical debt measure it, prioritize it, allocate capacity against itBuild Azure DevOps or GitHub Actions pipelines purpose-built for ML workflows (train-test-deploy automation)Manage R&D budgets, vendors, and external development partnersImplement blameless postmortem culture and learning loops tied to engineering OKRsStrategic GrowthIdentify emerging technologies, partnerships, and acquisition opportunities aligned to AI-first strategyContribute to executive strategy, long-term innovation planning, and board-level technology discussionsLead vendor consolidation reviews to rationalize AI/ML toolchain spendSuccess Measures (First 6 Months)DORA metrics baselined by Day 30; improvement targets set and tracked by Day 90Azure cloud migration/optimization roadmap delivered within 60 daysML pipeline operational within 90 days with at least one model in production servingMLOps maturity assessment completed by Day 45 with gap-to-target roadmapAI governance framework documented and adopted across R&D by Month 4At least two GenAI-powered features shipped to customers within 6 monthsData architecture modernization plan delivered within 90 days with executive sign-offEngineering team AI fluency baseline measured and upskilling program launched by Month 2Vendor consolidation review completed AI/ML toolchain spend rationalized within first quarterStrengthened engineering culture with measurable reduction in technical debtExperience & QualificationsRequired15+ years of experience in software engineering, R&D, or product development with progressive technical leadership5+ years in a senior leadership role leading cross-functional technical teams including ML/AI practitionersDirect experience building and scaling ML/AI pipelines in production environments (not just POCs)Hands-on expertise with Azure cloud platform (strongly preferred), including PaaS/IaaS architecture decisions, AKS, Azure AI ServicesWorking knowledge of MLOps toolchains: model registries, experiment tracking, automated retraining, drift detectionExperience with containerization (Docker, Kubernetes) and cloud-native deployment patternsStrong expertise in system architecture, API design, and microservices patternsTrack record improving engineering performance through modern development practices (CI/CD, observability, DORA metrics)Excellent executive communication, stakeholder alignment, and board-level presentation skillsComfortable operating at both strategic and hands-on levels including architecture reviews, code reviews, and technical decision-making
Job Title
VP R&D